System and method for predicting the timing of future service events of a product

ABSTRACT

A system and method for predicting timing of future service events of a product. A database contains a plurality of service information and performance information for the product. A statistical analyzer analyzes the plurality of processed service information to determine a plurality of compartment failure information. A performance deterioration rate analyzer analyzes the performance deterioration rate of the product from the plurality of service information and performance information. A simulator, simulates a distribution of future service events of the product according to the plurality of compartment failure information and the performance deterioration rate analysis.

BACKGROUND OF THE INVENTION

This disclosure relates generally to servicing products and systems andmore particularly to predicting the timing of future service events of aproduct or a system.

The market for long-term contractual agreements has grown at high ratesover recent years for many of today's service organizations. As theservice organizations establish long-term contractual agreements withtheir customers, it becomes important to understand the expected costsand risks associated with the pricing of service contracts and portfoliomanagement of the contracts. In addition, the service organizations needto have an understanding of the planning of repairs (shop workloadplanning) and how the introduction of new technology will affect theirservice contracts. In order to analyze these issues, it is necessary tocorrectly model the underlying behavior of the product or system so thateach can be serviced in the most cost-effective manner.

Currently available analytical practices are unable to accurately modelservice requirements for complex products or systems. Typically, thesemodels contain poor cost information which result in the serviceorganization inefficiently managing the risk associated with theirservice portfolios, failing to respond to customer needs and newtechnology, which all lead to lower long-term contract profitability. Astandard time-series method is one particular approach that has beenused to model the service requirements of repairable systems such asaircraft engines, automobiles, locomotives and other high tech products.This time-series method examines historical data obtained over a five toten year period and forms a trend line on either system costs and/ornumber of repairs made to the system. The trend line is then used topredict future costs and number of repairs. A limitation with this timeseries method is that it does not give details of failures at acompartmental level. A compartment is a physical or performance relatedsub-system of the repairable product, which when it fails causes theproduct to require maintenance or servicing. Other limitations with thestandard time series method is that it does not account for the lifecycle of the repairable product and thus does not provide a distributionof the expected service events for the product. An analysis based onengineering relationships to determine compartment parameters is anothermethod used to model the service requirements of repairable systems. Alimitation with this analysis is that it is not well based in underlyingstatistics, and thus cannot be shown to accurately model the repairableproduct on an ongoing basis.

In order to overcome the above problems, there is a need for an approachthat can model the service requirements of repairable systems that isaccurate and has a comprehensive statistical framework. Such an approachwill lead to better cost projections, more realistic and effective riskmanagement, new technology introduction and day-to-day service that ismore responsive to customer needs and higher long-term contractprofitability.

BRIEF SUMMARY OF THE INVENTION

In accordance with one embodiment of this disclosure, there is a systemfor predicting the timing of a future service event of a product formedfrom a plurality of compartments. The system comprises a database thatcontains a plurality of service information and a plurality ofperformance information for the product. A statistical analyzer analyzesthe plurality of service information to determine a plurality ofcompartment failure information. A performance deterioration rateanalyzer analyzes the performance deterioration rate of the product fromthe plurality of service information and performance information. Asimulator, simulates a distribution of future service events of theproduct according to the plurality of compartment failure informationand the performance deterioration rate analysis.

Similarly, in this disclosure there is a method for predicting thetiming of a future service event of a product formed from a plurality ofcompartments. The method comprises storing a plurality of serviceinformation and a plurality of performance information for the product;analyzing the plurality of service information to determine a pluralityof compartment failure information; performing a deterioration rateanalysis of the product from the plurality of service information andperformance information; and simulating a distribution of future serviceevents of the product according to the plurality of compartment failureinformation and the deterioration rate analysis.

Also, in this disclosure there is a computer-readable medium storingcomputer instructions for instructing a computer to predict the timingof a future service event of a product formed from a plurality ofcompartments. The computer instructions comprise storing a plurality ofservice information and a plurality of performance information for theproduct; analyzing the plurality of service information to determine aplurality of compartment failure information; performing a deteriorationrate analysis of the product from the plurality of service informationand performance information and simulating a distribution of futureservice events of the product according to the plurality of compartmentfailure information and the deterioration rate analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic of a general purpose computer system in which asystem for predicting the timing of future service events of a productoperates;

FIG. 2 shows a schematic diagram of a system for predicting the timingof future service events of a product that operates on the computersystem shown in FIG. 1;

FIG. 3 shows a flow chart describing actions performed by the systemshown in FIG. 2;

FIG. 4 shows a flow chart describing the actions performed by thestatistical analyzer shown in FIG. 2;

FIG. 5 shows a flow chart describing the actions performed by theperformance deterioration rate analyzer shown in FIG. 2;

FIGS. 6a-6 b show examples of plots that describe some of theperformance information stored in the performance historical databaseshown in FIG. 2;

FIG. 7 shows a flow chart describing the actions performed by thesimulator shown in FIG. 2; and

FIG. 8 shows a flow diagram describing the validating actions performedby the system shown in FIG. 2.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 shows a schematic of a general-purpose computer system 10 inwhich a system for predicting the timing of future service events of aproduct operates. The computer system 10 generally comprises a processor12, a memory 14, input/output devices, and data pathways (e.g., buses)16 connecting the processor, memory and input/output devices. Theprocessor 12 accepts instructions and data from the memory 14 andperforms various calculations. The processor 12 includes an arithmeticlogic unit (ALU) that performs arithmetic and logical operations and acontrol unit that extracts instructions from memory 14 and decodes andexecutes them, calling on the ALU when necessary. The memory 14generally includes a random-access memory (RAM) and a read-only memory(ROM), however, there may be other types of memory such as programmableread-only memory (PROM), erasable programmable read-only memory (EPROM)and electrically erasable programmable read-only memory (EEPROM). Also,the memory 14 preferably contains an operating system, which executes onthe processor 12. The operating system performs basic tasks that includerecognizing input, sending output to output devices, keeping track offiles and directories and controlling various peripheral devices.

The input/output devices comprise a keyboard 18 and a mouse 20 thatenter data and instructions into the computer system 10. A display 22allows a user to see what the computer has accomplished. Other outputdevices could include a printer, plotter, synthesizer and speakers. Amodem or network card 24 enables the computer system 10 to access othercomputers and resources on a network. A mass storage device 26 allowsthe computer system 10 to permanently retain large amounts of data. Themass storage device may include all types of disk drives such as floppydisks, hard disks and optical disks, as well as tape drives that canread and write data onto a tape that could include digital audio tapes(DAT), digital linear tapes (DLT), or other magnetically coded media.The above-described computer system 10 can take the form of a hand-helddigital computer, personal digital assistant computer, personalcomputer, workstation, mini-computer, mainframe computer andsupercomputer.

FIG. 2 shows a schematic diagram of a system 28 for predicting thetiming of future service events of a product that operates on thecomputer system 10 shown in FIG. 1. In the system 28, a service database30 stores a plurality of service information for the product. Theplurality of service information varies depending on the product.Generally, the plurality of service information will compriseinformation such as compartment definitions of the product (i.e., aphysical or performance related subsystem that is considered as a unit,which when it fails requires that the product needs maintenance orservicing), repair history of the product (e.g., dates of serviceevents, types of service events, time for a compartment to fail, etc.),as well as any factors which may play a role in explaining the length oftime which passes between service events (e.g., environment, operatingconditions of the product, product configuration, equipment age, etc.).Other factors may include types of maintenance, cycle time of theproduct, usage of the product, contract terms and conditions, equipmentage and vintage, etc. In this disclosure, the product will be describedwith reference to an aircraft engine; however, other products such as apower system, a locomotive or any other electrical, chemical ormechanical products, where it is desirable to predict the timing offuture service events, may be used.

Referring to FIG. 2, a preprocessor 32 processes the plurality ofservice information into a predetermined format. The preprocessingincludes extracting the plurality of service information from theservice database 30; assigning each data record in the database to acompartment depending upon the engineering labeled removal cause;creating new variables from existing variables (e.g., censoringvariables, customer indicator variables), deleting outliers andproducing summary statistics of the data set (e.g., number of recordsfor each compartment). After performing these acts, the preprocessor 32generates a plurality of data files according to the serviceinformation, wherein each of these data files are formatted as SAS datasets.

A statistical analyzer 34 analyzes the plurality of processed serviceinformation to determine a plurality of compartment failure information.The compartment failure information may include statisticallysignificant compartment failure variables and the associated compartmenttime-to-failure coefficients. Compartment failure variables arevariables that affect the time between service or maintenance events.For example, in the aircraft engine scenario, the thrust rating of theengine and the environment that the engine flies in are examples ofpossible statistically significant compartment failure variables.Compartment time-to-failure coefficients are coefficients that areapplied to each of the compartment failure variables. The compartmentfailure variables and associated compartment time-to-failurecoefficients are used to determine the time between servicing events forthe compartments. In addition, the statistical analyzer 34 uses thisinformation to determine which compartment failure variables influenceservice events and estimate time-to-failure distributions for thecompartments.

The statistical analyzer 34 comprises several scripts that enable it toperform the aforementioned functions as well as some additionalfunctions. One particular script that the statistical analyzer 34 usesis a service analysis script that executes a plurality of statisticalprocedures. The plurality of statistical procedures may comprise amulti-variate regression and/or a correlation analysis. Both themulti-variate regression and correlation can determine which compartmentfailure variables influence the timing of service events and estimatethe time-to-failure distributions for the compartments. The statisticalanalyzer 34 uses other scripts to output this information as well asstatistical diagnostics 36 and residual plots 38. The output from themulti-variate regression and/or correlation analysis may include thecompartment time-to-failure coefficients for each compartment associatedwith the product. Other standard output from the multi-variateregression and/or correlation analysis may include a standard errorassociated with each of the compartment time-to-failure coefficients anda P value, which is an indication of whether a particular variable has asignificant effect on the time between occurrences of service events.

Statistical diagnostics that may be outputted include goodness-of-fitmetrics and collinearity diagnostics. These enable a user to determinethe most appropriate compartment model in the statistical analyzer 34.Residual plots enable a user of the system 28 to determine how well theregression model fits the service information data. Generally, theresidual plots are defined as the difference between the actual timeuntil servicing values and the predicted time until servicing values. Asmall residual value is an indication that the regression or correlationanalysis was a good fit, whereas a large residual value is an indicationthat the fit may be improved.

In addition to residual plots, the statistical analyzer 34 may useanother script to output information such as probability plots, whichenables one to assess whether the assumed life distribution for eachcompartment is appropriate or not. Another script may be used togenerate a plot of residuals versus each variable that affects the timethat a service event occurs.

Referring again to FIG. 2, the system 28 also comprises a performancehistorical database 40 that includes a plurality of performanceinformation obtained from the product while in operation. As mentionedabove, this disclosure is described with reference to an aircraftengine. Therefore, the performance information can be acquired by usingany of a plurality of data acquisition devices such as sensors andtransducers. After the data have been obtained, the data acquisitiondevices can transfer the data to a remote monitoring facility forstorage and evaluation. Also, it is possible to have the data from thedata acquisition devices manually recorded and entered into theperformance historical database 40. The plurality of performanceinformation includes but is not limited to information such asperformance characteristic values (e.g., exhaust gas temperature, EGT),initial data levels after servicing, current data levels, dates at whichthe product is serviced, and variables that affect the servicing of asubset of compartments of the product. For this aircraft engineembodiment, the variables may include flight leg, engine thrust,customer, engine model, engine series, etc. All of this performanceinformation is described below in more detail.

A performance deterioration rate analyzer 42 analyzes the performancecharacteristic values of the product from both the plurality of serviceinformation and performance information. The performance deteriorationrate analyzer 42 comprises a statistical analysis script that relates asubset of compartments of the product according to time. For purposes ofdescribing the performance deterioration rate analyzer 42, time is theamount of time that the aircraft engine is in use. Time can be measuredby variables such as cycles or hours. The statistical analysis scriptgenerates an estimated deterioration rate curve for a subset ofcompartments of the product. The performance deterioration rate analyzer42 further comprises a transformer that transforms each estimateddeterioration rate curve for a compartment to a performance lifedistribution. A performance life distribution is a statisticaldistribution representing the statistical properties of the time betweenservicing events and is estimated using performance data as opposed toservice data. The performance life distribution is in the same form asthe estimated time-to-failure distributions for the compartmentsdetermined by the statistical analyzer 34.

A simulator 44 simulates the future service events of the productaccording to the plurality of compartment failure information generatedby the statistical analyzer 34 and the performance life distributiongenerated by the performance deterioration rate analyzer 42. Thesimulation results in a forecast of the timing of the future serviceevents. In particular, the simulator 44 takes the compartmenttime-to-failure coefficients from the statistical analyzer 34 anddetermines a Weibull distribution for each compartment defined for theproduct. Also, the simulator 44 takes the performance life distributionfrom the performance deterioration rate analyzer 42 and determines aWeibull distribution for each of the associated compartments defined forthe product. The simulator 44 then uses the compartment distributions todetermine the overall distribution for the product. Generally, thesimulator uses a discrete event-driven Monte Carlo simulation to performthe above operations. After performing the simulation, the simulator 44generates several outputs. For instance, one output is the contractoutput 46, which typically comprises the following: maintenance eventdistribution parameters over time; demand distributions spare/leasedequipment; and equipment performance distributions (e.g., aircraftengine time on wing). The simulator 44 is not limited to these outputsand it is possible to use other outputs if desired.

In order to understand the performance of the simulator 44, the system28 uses a validator, which can be part of the statistical analyzer 34 orthe simulator or separate from both. The validator contains a validationscript prepared for a case study done for the product. For purposes ofthis disclosure, a case study is defined as any subset of historicalservice event data that is used for model validation. For example, theservice events that took place on a group of randomly chosen systemsover the past year may serve as a case study, and during validation, acomparison is a made between the number of service events projected bythe model for these systems over this period of time and the actualnumber of service events that took place. The validation script willcompare the compartment distributions determined by the simulator 44 tothe distributions that actually happened in the case study. After makingthe comparison, the validator generates a series of graphical outputs 48on availability and reliability. In a preferred embodiment, three setsof reliability graphs are generated. The first set of reliability graphsare relative frequency histograms of the actual compartmentdistributions for each of the first four shop visits for the case study.Overlaid on these relative frequency histograms are the compartmentdistributions determined by the simulator 44. The second set ofreliability graphs are relative frequency histograms of the actualsystem level distributions for each of the first four shop visits forthe case study. Overlaid on each of these relative frequency histogramsare the system distributions determined by the simulator 44. The thirdset of reliability graphs are non-parametric Kaplan-Meier estimatedsurvival curves determined from both the actual system leveldistribution and the system level distribution determined by thesimulator 44 for each of the first four shop visits. From these outputs,a user can generate a service plan forecast for the product thatcomprises time for scheduling service events.

FIG. 3 shows a flow chart describing actions performed by the system 28shown in FIG. 2. At block 50, a plurality of service information andperformance information for the product stored in the service databaseand the performance historical database, respectively, are obtained. Thepreprocessor preprocesses the plurality of service information into apredetermined format at 52. The statistical analyzer analyzes theplurality of processed service information to determine a plurality ofcompartment failure information at 54. In particular, the statisticalanalyzer determines both the compartment time-to-failure coefficientsand the compartment failure variables using the aforementionedstatistical procedures. The statistical analyzer outputs the compartmenttime-to-failure coefficients and the compartment failure variables tothe simulator and generates various residual plots.

At the same time the service information are being preprocessed andanalyzed by the preprocessor and the statistical analyzer, theperformance information are simultaneously evaluated by the performancedeterioration rate analyzer. If desired, it is also possible to have theperformance information preprocessed in a manner similar to the serviceinformation. Regardless of whether the performance information arepreprocessed, the performance deterioration rate analyzer runs adeterioration rate analysis at 56. As mentioned above, the deteriorationrate analysis generates an estimated deterioration rate curve for asubset of compartments of the product and transforms each estimateddeterioration rate curve to a performance life distribution.

After analyzing the service information and the performance information,the simulator simulates the future service events of the productaccording to the compartment failure information and the performancelife distribution at 58. In addition, the simulator forecasts orpredicts the timing of the future service events at 60. As mentionedabove, this information is in the form of distributions for eachcompartment that makes up the product. The validator compares thecompartment distributions determined by the simulator to thedistributions that actually happened in the case study at 62. Aftermaking the comparison, the validator generates a series of graphicaloutputs on availability and reliability.

FIG. 4 shows a flow chart describing the actions performed by thestatistical analyzer shown in FIG. 2. At block 64, the statisticalanalyzer obtains the service information from the preprocessor. Thestatistical analyzer then generates compartment definitions for theservice information at 66. At 68, the statistical analyzer determinescompartment failure information such as the statistically significantcompartment failure variables and their associated compartmenttime-to-failure coefficients using the aforementioned statisticalprocedures. The statistical analyzer then applies the compartmenttime-to-failure coefficients to the compartment failure variables at 70.At block 72, the statistical analyzer generates the various statisticaldiagnostics for each compartment associated with the product. At block74, the statistical analyzer generates residual plots and probabilityplots and other types of plots if desired. As mentioned above, thestatistical analyzer can generate other information such as standarderrors associated with each of the compartment time-to-failurecoefficients and a P value.

FIG. 5 shows a flow chart describing the actions performed by theperformance deterioration rate analyzer 42 shown in FIG. 2. Beforerunning the deterioration rate analysis, the performance deteriorationrate analyzer first obtains the plurality of service information andperformance information from the service database and the performancehistorical database, respectively, at 76. As mentioned earlier, theplurality of performance information includes information such asperformance characteristic values, initial data levels after servicing,current data levels, dates at which the product is serviced, andvariables that affect the servicing of each compartment of the product.

FIGS. 6a-6 b show examples describing some of the above-mentionedperformance information. In particular, FIG. 6a shows an example of adeterioration rate curve for a compartment of an aircraft engine. Inthis example, the compartment is EGT, however, other compartments couldbe used. For instance, an illustrative but non-exhaustive list couldinclude delta exhaust gas temperature (dEGT), which is the deviationfrom the baseline EGT, fuel flow (WF), core speed (N2), and EGTdivergence, divEGT, which is the difference of the EGT between the rawEGT of the engine in question and the mean of raw EGT of all engines.The deterioration curve shows the degradation of the compartment overtime. Eventually, after a period of time, the compartment reaches alevel that the degradation is severe enough to warrant servicing. Theinitial data level performance parameter is the initial level of thecompartment after being serviced. In FIG. 6a, the initial level is about50° F., and over time the level of the EGT margin will degrade. Thehistorical trending levels are shown in FIG. 6a as data points. FIG. 6bshows an example of the raw EGT levels. In particular, FIG. 6b shows aplot illustrating the removal level or redline for the EGT compartment.The removal level indicates an absolute time that the compartmentreaches a predetermined level that necessitates the removal of theaircraft engine for servicing.

Referring back to FIG. 5, after the service information and theperformance information are obtained, the performance deterioration rateanalyzer executes the statistical analysis script that relates eachcompartment of the product according to time at 78. Preferably, thestatistical analysis runs a multi-variate regression analysis for eachcompartment of the product to identify variables that influence the timebetween servicing events. An illustrative example of a multi-variateregression analysis using a Weibull distribution is presented. The time(specified either in engine flight hours or engine cycles) betweenservice events is represented as Y. As mentioned earlier, thrust (X1)and flight leg (X2) are two compartment failure variables that mightinfluence the time between service events, Y. The multi-variate Weibullregression model takes the form:

ln (Y)=α+β₁ X1+β₂ X2+σε,

where ln is the natural log function, ε is an error term which followsthe smallest extreme value distribution, and α, β₁, β₂, and σ arecompartment failure parameters to be estimated from the service data.

For fixed values of thrust (X1) and flight leg (X2), a Weibulldistribution representing time between servicing events may bedetermined. For example, if α=8.9, β₁=−0.00003, β₂=0.75, and σ=0.5 whereX1=23500 and X2=1.8, the Weibull distribution of the time betweenservicing events for this compartment would have a location (or scale)parameter (i.e., the 63.2^(nd) failure percentile) ofexp(8.9−0.00003*23500+0.75*1.8)=13975 and a shape parameter equal to1/σ=1/0.50=2.0. Although a Weibull regression analysis is described,other statistical analyses such as multiple non-linear and loglinearanalyses could also be used.

The performance deterioration rate analyzer generates estimateddeterioration rate curves for a subset of compartments of the product at80. The estimated deterioration rate curves are determined using amulti-variate regression and/or correlation statistical analysis. Denotethe performance characteristic (e.g., EGT margin) as Y. One example of amulti-variate regression and/or correlation analysis is presented usingY and time (as measured in cycles) in the following model:

Y=α+β ₁Cycles+ε

where ε follows a normal distribution and α and β₁ are parameters to beestimated from the performance and service information data. Theestimate of β₁ in this example is an estimated rate of deterioration forthe performance characteristic, Y. For example, if β₁=0.003 then theperformance characteristic, Y, deteriorates at a approximately 3 degreesper 1000 cycles.

Next, the performance deterioration rate analyzer transforms eachestimated deterioration rate curve for the respective compartment to aperformance life distribution at 82. The performance life distributionis characterized by a location (or scale) parameter and a shapeparameter. In order to perform the transformation, estimates of α, β₁,and a performance characteristic limit value (i.e., a value at which thecompartment requires servicing) which we denote by EGTL, are required.The location (or scale) parameter of the performance life distributionis obtained using the following formula:${Location} = {\exp( \frac{\begin{matrix}{{{\ln ( \frac{{EGTL} - \alpha}{\beta_{1}} )}*{\ln ( {- {\ln ( {1 - 0.825} )}} )}} -} \\{{\ln ( \frac{{EGTL} - \alpha}{\beta_{1} - {\beta_{1}/3}} )}*{\ln ( {{- \ln}( {1 - 0.50} )} )}}\end{matrix}}{{\ln ( {- {\ln ( {1 - 0.825} )}} )} - {\ln ( {- {\ln ( {1 - 0.5} )}} )}} )}$

The shape parameter of the performance life distribution is obtainedusing the following formula:${Shape} = {\frac{\ln ( {- {\ln ( {1 - 0.825} )}} )}{{\ln ( \frac{{EGTL} - \alpha}{\beta_{1} - {\beta_{1}/3}} )} - {\ln ({Location})}}.}$

As an example, suppose the estimate of α is 0, the estimate of β₁=0.003and the performance characteristic value is 60. Using the formula above,the location parameter value is estimated to be 23498. The shapeparameter is estimated to be 2.27. The performance life distributionsfor all compartments are then transferred to the simulator at 84.

FIG. 7 shows a flow chart describing the actions performed by thesimulator 44 shown in FIG. 2. As mentioned earlier, the simulator 44 isinterested in determining the distribution of failures at the product'ssystem level so that the timeliness of future service events can bepredicted. The simulator 44 is able to determine the distribution offailures at the product's system level because of the informationprovided by the statistical analyzer 34 and the performancedeterioration rate analyzer 42. The information (i.e., time-to-failurecoefficients and compartment variables) provided by the statisticalanalyzer 34 facilitates an understanding of each of the compartmentsthat make up the product's system level and their relationship with eachother, while the performance life distributions provided by theperformance deterioration rate analyzer gives more information aboutprobable service requirements. The simulator 44 uses this information toexamine the system or aggregate level and predict the overall timing ofservice events for the product. Referring back to FIG. 7, the actionsperformed by the simulator begin at block 86, where random failure times(i.e., service events) for each compartment distribution are generated.From the randomly generated failure times, the minimum of these valuesis found at 88. The simulator designates t_(i) as the minimum time,where i is the compartment associated with this time value. Thesimulator then records the minimum time t_(i) as the next failure time(i.e., time for a service event) for the system level at 90. At 92, thesimulator determines whether there are any more system level failuresneeded. If so, then blocks 86-90 are repeated a large number of times.Once all of the iterations have been performed, the simulator forms asystem level distribution from the failure times at 94. At 96, thesimulator generates the output tables and the input report, while agraphical output is generated at 98.

FIG. 8 shows a flow diagram describing the validating actions performedby the system shown in FIG. 1. In this diagram, historical service eventdata and the performance historical data are stored in a database at100. After identifying a case study, the historical service event dataand performance historical data are separated out according to the casestudy at 102. If the historical service event data and performancehistorical data are not in the case study, then this data are used tobuild a model as described in FIG. 2 at 104. Project service incidentsalong with statistical confidence bounds that should take place in thecase study are determined at 106. An example of the project serviceincidents along with statistical confidence bounds are shown at 108. Theservice incidents are compared to data that are used in the case studyat 110. If the actual data do not match the projection, then the modelneeds to be reexamined as noted at 112. On the other hand, if the datado match the projection within the statistical confidence bounds, thenthe model is validated as noted at 114.

The foregoing flow charts of this disclosure show the architecture,functionality, and operation of a possible implementation of the systemfor predicting the timing of future service events of a product. In thisregard, each block represents a module, segment, or portion of code,which comprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that in somealternative implementations, the functions noted in the blocks may occurout of the order noted in the figures, or for example, may in fact beexecuted substantially concurrently or in the reverse order, dependingupon the functionality involved.

The above-described system and method for predicting the timing offuture service events of a product comprise an ordered listing ofexecutable instructions for implementing logical functions. The orderedlisting can be embodied in any computer-readable medium for use by or inconnection with a computer-based system that can retrieve theinstructions and execute them. In the context of this application, thecomputer-readable medium can be any means that can contain, store,communicate, propagate, transmit or transport the instructions. Thecomputer readable medium can be, for example but not limited to, anelectronic, magnetic, optical, electromagnetic, infrared system,apparatus, or device. An illustrative, but non-exhaustive list ofcomputer-readable mediums can include an electrical connection(electronic) having one or more wires, a portable computer diskette(magnetic), a random access memory (RAM) (magnetic), a read-only memory(ROM) (magnetic), an erasable programmable read-only memory (EPROM orFlash memory) (magnetic), an optical fiber (optical), and a portablecompact disc read-only memory (CDROM) (optical). It is even possible touse paper or another suitable medium upon which the instructions areprinted. For instance, the instructions can be electronically capturedvia optical scanning of the paper or other medium, then compiled,interpreted or otherwise processed in a suitable manner if necessary,and then stored in a computer memory.

It is apparent that there has been provided in accordance with thisinvention, a system and method for predicting the timing of futureservice events of a product. While the invention has been particularlyshown and described in conjunction with a preferred embodiment thereof,it will be appreciated that variations and modifications can be effectedby a person of ordinary skill in the art without departing from thescope of the invention.

What is claimed is:
 1. A system for predicting the timing of a futureservice event of a product formed from a plurality of compartments,comprising: a database that contains a plurality of service informationand a plurality of performance information for the product; astatistical analyzer that analyzes the plurality of service informationto determine a plurality of compartment failure information comprisingcompartment failure variables and compartment time-to-failurecoefficients, wherein the stastistical analyzer uses the plurality ofcompartment failure information to determine which compartment failurevariables influence the timing of future service events and estimatetime-to-failure distributions for the plurality of compartments; aperformance deterioration rate analyzer that analyzes performancedeterioration rate of the product from the plurality of serviceinformation and performance information, wherein the performancedeterioration rate analyzer comprises a statistical analysis script thatrelates performance information of a subset of compartments of theproduct according to time, wherein the statistical analysis scriptgenerates an estimated deterioration rate curve for the subset ofcompartments of the product, wherein the performance deterioration rateanalyzer further comprises a transformer that transforms each estimateddeterioration rate curve for a compartment to a performance lifedistribution; and a simulator for simulating a distribution of futureservice events of the product according to the time-to-failuredistributions and performance life distributions plurality.
 2. Thesystem according to claim 1, wherein the database comprises a servicedatabase and a performance historical database.
 3. The system accordingto claim 1, wherein the plurality of performance information comprisescompartment definitions, repair history and service factors.
 4. Thesystem according to claim 1, wherein the plurality of performanceinformation comprises performance characteristic values, initial datalevels after servicing, currant data levels, dales at which the productis serviced, and variables that affect the servicing of a subset of theplurality of compartments.
 5. The system according in claim 1, furthercomprising a preprocessor for processing the plurality of serviceinformation into a predetermined format.
 6. The system according toclaim 5, wherein the preprocessor generates a plurality of data filesaccording to the plurality of service information.
 7. The systemaccosting to claim 1, wherein the statistical analyzer uses theestimated time-to-fallure distributions to determine a Weibulldistribution for a subset of the plurality of compartments defined tothe product.
 8. The system according to claim 1, wherein the statisticalanalyzer comprises a service analysis script that executes a pluralityof statistical procedures.
 9. The system according to claim 8, whereinthe plurality of statistical procedures comprise a multivariateregression and/or a correlation analysis.
 10. The system according toclaim 8, wherein the service analysis script generates a plurality ofstatistical diagnostic information.
 11. The system according to claim10, wherein the plurality of statistical diagnostic informationcomprises goodness-of-fit metrics and collinearity diagnostics.
 12. Thesystem according to claim 8, wherein the service analysis scriptgenerates a plurality of residual plots.
 13. The system according toclaim 1, wherein the statistical analyzer comprises a validation script.14. The system according to claim 13, wherein the validation script isapplied to a plurality of case studies set up for the product.
 15. Thesystem according to claim 1, wherein the simulator uses the performancelife distributions to determine a Weibull distribution for a subset ofthe plurality of compartments defined for the product.
 16. The systemaccording to claim 1, wherein the simulator forecasts a service plan forthe future service events that comprises the time for scheduling theservice events.
 17. A system for predicting the timing of a futureservice event of a product formed from a plurality of compartments,comprising: means for containing a plurality of service information enda plurality of performance information for the product; means foranalyzing the plurality of service information to determine a pluralityof compartment failure information comprising compartment failurevariables and compartment time-to-failure coefficients, wherein theanalyzing means uses the plurality of compartment failure information todetermine which compartment failure variables influence the timing offuture service events and estimate time-to-failure distributions for thecompartments; means for performing a deterioration rate analysis thatdetermines performance deterioration rate of the product from theplurality of service information and performance information, whereinthe performing means comprises a statistical analysis script thatrelates performance information of a subset of the plurality ofcompartments of the product according to time, wherein the statisticalanalysis script generates an estimated deterioration rate curve for asubset of the plurality of compartments of the product, wherein theperforming means further comprises means for transforming each estimateddeterioration rate curve for a compartment to a performance lifedistribution; and means for simulating a distribution of future serviceevents of the product according to the time-to-failure distributions andperformance life distributions plurality of compartment.
 18. The systemaccording to claim 17, wherein the plurality of service informationcomprises compartment definitions, repair history and service factors.19. The system according to claim 17, wherein the plurality ofperformance information comprises performance characteristic values,initial data levels after servicing, current data levels, dates at whichthe product is serviced, and variables that effect the servicing of asubset of the plurality of compartments of the product.
 20. The systemaccording to claim 17, further comprising means for preprocessing theplurality of service information into a predetermined format.
 21. Thesystem according to claim 17, wherein the preprocessing means generatesa plurality of data files according to the plurality of serviceinformation.
 22. The system according to claim 17, wherein the analyzingmeans uses the estimated time-to-failure distributions to determine aWeibull distribution for a subset of the plurality of compartmentsdefined for the product.
 23. The system according to claim 17, whereinthe analyzing means comprises a service analysis script that executes aplurality of statistical procedures.
 24. The system according to claim23, wherein the plurality of statistical procedures comprise amultivariate regression and/or a correlation analysis.
 25. The systemaccording to claim 23, wherein the service analysis script generates aplurality of statistical diagnostic information.
 26. The systemaccording to claim 25, wherein the plurality of statistical diagnosticinformation comprises goodness-of-fit metrics and collinearitydiagnostics.
 27. The system according to claim 23, wherein the serviceanalysis script generates a plurality of residual plots.
 28. The systemaccording to claim 17, wherein the analyzing means comprises avalidation script.
 29. The system according to claim 28, wherein thevalidation script is applied to a plurality of case studies set up forthe product.
 30. The system according to claim 17, wherein the simulatoruses the performance life distribution to determine a Weibulldistribution for a subset of the plurality of compartments defined forthe product.
 31. The system according to claim 17, wherein the simulatorforecasts a service plan for the future service events that comprisesthe time for scheduling the service events.
 32. A method for predictingthe timing of a future service event of a product formed from aplurality of compartments, comprising; storing a plurality of serviceinformation and a plurality of performance information for the product;analyzing the plurality of service information to determine a pluralityof compartment failure information comprising compartment failurevariables and compartment time-to-failure coefficients, wherein theanalyzing uses the plurality of compartment failure information todetermine which compartment failure variables influence the timing offuture service events and estimate time-to-failure distributions for theplurality of compartments; performing a deterioration rate analysis ofthe product from the plurality of service information and performanceinformation, wherein the performing comprises using a statisticalanalysis script that relates performance information of a subset of theplurality of compartments of the product according to time, wherein thestatistical analysis script generates an estimated deterioration ratecurve for a subset of the plurality of compartments of the product,wherein the performing a deterioration rate analysis further comprisestransforming each estimated deterioration rate curve for a compartmentto a performance life distribution; and simulating a distribution offuture service events of the product according to the time-to-failuredistributions and performance life distributions.
 33. The methodaccording to claim 32, wherein the plurality of service informationcomprises compartment definitions, repair history and service factors.34. The method according to claim 32, wherein the plurality ofperformance information comprises performance characteristic values,initial data levels after servicing, current data levels, dates at whichthe product is serviced, and variables that affect the servicing of asubset of the plurality of compartments of the product.
 35. The methodaccording to claim 32, further comprising preprocessing the plurality ofservice information into a predetermined format.
 36. The methodaccording to claim 35, wherein the preprocessing generates a pluralityof data files according to the plurality of service information.
 37. Themethod according to claim 32, wherein the analyzing uses the estimatedtime-to-failure distributions to determine a Weilbull distribution for asubset of the plurality of compartments.
 38. The method according toclaim 32, wherein the analyzing comprises using a service analysisscript that executes a plurality of statistical procedures.
 39. Themethod according to claim 38, a wherein the plurality of statisticalprocedures comprise a multivariate regression and/or a correlationanalysis.
 40. The method according to claim 39, wherein the serviceanalysis script generates a plurality of statistical diagnosticinformation.
 41. The method according to claim 40, wherein the pluralityof statistical diagnostic intonation comprises goodness-of-fit metricsand collinearity diagnostics.
 42. The method according to claim 38,wherein the service analysis script generating a plurality of residualplots.
 43. The method according to claim 32 wherein the analyzingcomprises using a validation script.
 44. The method according to claim43, wherein the validation script is applied to a plurality of casestudies set up for the product.
 45. The method according to claim 32,wherein the simulating uses the performance life distributions todetermine a Weibull distribution for a subset of the plurality ofcompartments.
 46. The method according to claim 32, wherein thesimulating forecasts a service plan for the future service events thatcomprises the time for scheduling the service events.
 47. Acomputer-readable medium storing computer instructions which whenexecuted on a computer system predict the timing of a future serviceevent of a product formed from a plurality of compartments, the computerinstructions comprising: storing a plurality of service information anda plurality of performance information for the product; analyzing theplurality of service information to determine a plurality of compartmentfailure information comprising compartment failure variables andcompartment time-to-failure coefficients, wherein the analyzinginstructions uses the plurality of compartment failure information todetermine which compartment failure variables influence the timing offuture service events and estimates time-to-failure distributions forthe plurality of compartments; performing a deterioration rate analysisof the product from the plurality of service information and performanceinformation, wherein the performing instructions comprise using astatistical analysis script that relates performance information of asubset of the plurality of compartments of the product according totime, wherein the statistical analysis script generates an estimateddeterioration rate curve for a subset of the plurality of compartmentsof the product, wherein the performing instructions further comprisetransforming instructions that transform each estimated deteriorationrate curve to a performance life distribution; and simulating adistribution of future service events of the product according to thetime-to-failure distributions and performance life distributions. 48.The computer-readable medium according to claim 47, wherein theplurality of service information comprises compartment definitions,repair history and service factors.
 49. The computer-readable mediumaccording to claim 47, wherein the plurality of performance informationcomprises performance characteristic values, initial data levels afterservicing, current data levels, dates at which the product is serviced,and variables that affect the servicing of a subset of the plurality ofcompartments of the product.
 50. The computer-readable medium accordingto claim 47, further comprising preprocessing instructions thatpreprocess the plurality of service information into a predeterminedformat.
 51. The computer-readable medium according to claim 50, whereinthe preprocessing instructions generates a plurality of data filesaccording to the plurality of service information.
 52. Thecomputer-readable medium according to claim 47, wherein the analyzinginstructions use the estimated time-to-failure distributions todetermine a Weibull distribution for a subset of the plurality ofcompartments.
 53. The computer-readable medium according to claim 47,wherein the analyzing instructions comprises instructions for using aservice analysis script that executes a plurality of statisticalprocedures.
 54. The computer-readable medium according to claim 53,wherein the plurality of statistical procedures comprise a multivariateregression and/or a correlation analysis.
 55. The computer-readablemedium according to claim 54, wherein the service analysts scriptgenerates a plurality of statistical diagnostic information.
 56. Thecomputer-readable medium according to claim 55, wherein the plurality ofstatistical diagnostic information comprises goodness-of-fit metrics andcollinearity diagnostics.
 57. The computer-readable medium according toclaim 53, wherein the service analysis script generates a plurality ofresidual plots.
 58. The computer-readable medium according to claim 47,wherein the analyzing instructions comprise using a validation script.59. The computer-readable medium according to claim 58, wherein thevalidation script is applied to a plurality of case studies set up forthe product.
 60. The computer-readable medium according to claim 47,wherein the simulating instructions use the performance lifedistribution to determine a Weibull distribution for a subset of theplurality of compartments.
 61. The computer-readable medium according toclaim 47, wherein the simulating instructions forecasts a service planfar the future service events that comprises the time for scheduling theservice events.